Measuring the accuracy and interest of association rules: A new framework

Galiano, Fernando Berzal; Blanco, Ignacio J.; Sánchez, Daniel; Miranda, María Amparo Vila

doi:10.3233/ida-2002-6303

Cited by 129 publications

(62 citation statements)

References 5 publications

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“…This property, that was proved for ARs in Berzal et al (2002) holds for ADs as well, as the following proposition shows:…”

Section: Definition 26mentioning

confidence: 65%

“…Despite it is the most employed, confidence is not a suitable accuracy measure for association rules, as pointed out by different authors (Brin et al 1997, Silverstein et al 1998, Berzal et al 2002. For instance, Piatetsky-Shapiro (1991) suggested that a suitable accuracy measure ACC for ADs should verify the following properties:…”

Section: An Alternative Accuracy Measurementioning

confidence: 96%

“…In addition, we can point out some relationships to other measures in the literature that are shown in Berzal et al (2002):…”

Section: Definition 26mentioning

confidence: 98%

“…Among other problems, confidence is not able to detect neither statistical independence nor negative dependence between sets of items (Silverstein et al 1998, Berzal et al 2002.…”

Section: An Alternative Accuracy Measurementioning

confidence: 99%

“…In Berzal et al (2002) we show that certainty factor (Shortliffe and Buchanan 1975), a rule uncertainty measure coming from the expert systems field and developed for the MYCIN system, is an alternative accuracy measure for ARs with good properties.…”

Section: An Alternative Accuracy Measurementioning

confidence: 99%

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Using association rules to mine for strong approximate dependencies

Sánchez

Serrano

Blanco

et al. 2008

Data Min Knowl Disc

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In this paper we deal with the problem of mining for approximate dependencies (AD) in relational databases. We introduce a definition of AD based on the concept of association rule, by means of suitable definitions of the concepts of item and transaction. This definition allow us to measure both the accuracy and support of an AD. We provide an interpretation of the new measures based on the complexity of the theory (set of rules) that describes the dependence, and we employ this interpretation to compare the new measures with existing ones. A methodology to adapt existing association rule mining algorithms to the task of discovering ADs is introduced. The adapted algorithms obtain the set of ADs that hold in a relation with accuracy and support greater than user-defined thresholds. The experiments we have performed show that our approach performs reasonably well over large databases with real-world data.

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“…This property, that was proved for ARs in Berzal et al (2002) holds for ADs as well, as the following proposition shows:…”

Section: Definition 26mentioning

confidence: 65%

Section: An Alternative Accuracy Measurementioning

confidence: 96%

“…In addition, we can point out some relationships to other measures in the literature that are shown in Berzal et al (2002):…”

Section: Definition 26mentioning

confidence: 98%

“…Among other problems, confidence is not able to detect neither statistical independence nor negative dependence between sets of items (Silverstein et al 1998, Berzal et al 2002.…”

Section: An Alternative Accuracy Measurementioning

confidence: 99%

Section: An Alternative Accuracy Measurementioning

confidence: 99%

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Using association rules to mine for strong approximate dependencies

Sánchez

Serrano

Blanco

et al. 2008

Data Min Knowl Disc

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Fuzzy frameworks for mining data associations: fuzzy association rules and beyond

Marín

Ruiz

Sánchez

2016

WIREs Data Min & Knowl

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Looking for associations in data is one of the data mining tasks that has aroused more interest in the literature. In this area, incorporating concepts of fuzzy set theory is useful in problems where imprecision and/or uncertainty appear. In most of the existing approaches, fuzzy association rules are widely seen as fuzzy rules, which are very different in nature from association rules, so problems like fuzzy inclusion and cardinality have been seldom taken into account explicitly, mostly providing ad hoc solutions for capturing semantics. In contrast, in this study we have taken the more general and natural approach of considering the elements of the association rule mining framework and studying possible and sensible fuzzy extensions, referred here as fuzzy frameworks for mining associations. As fuzzy frameworks are abstract mathematical models, another key contribution of the article is the notion of interpretations as mappings between fuzzy frameworks and specific datasets. This general analysis of the field is completed with a study of various important aspects that arise when proposing quality measures in a fuzzy environment, as well as those related to computational issues. The work also includes a review of the fuzzy framework that arises from fuzzy transactions regarded as fuzzy subsets of items, and shows that many of the approaches on fuzzy association rules that exist in the literature can be placed in the context of a proper interpretation of that framework. WIREs Data Mining Knowl Discov 2016, 6:50–69. doi: 10.1002/widm.1176 This article is categorized under: Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Technologies > Association Rules

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Temporal association rule mining: An overview considering the time variable as an integral or implied component

Segura‐Delgado

Gacto

Alcalá

et al. 2020

WIREs Data Min & Knowl

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Association rules are commonly used to provide decision‐makers with knowledge that helps them to make good decisions. Most of the published proposals mine association rules without paying particular attention to temporal information. However, in real‐life applications data usually change over time or presenting different temporal situations. Therefore, the extracted knowledge may not be useful, since we may not know whether the rules are currently applicable or whether they will be applicable in the future. For this reason, in recent years, many methods have been proposed in the literature for mining temporal association rules, which introduce a greater predictive and descriptive power providing an additional degree of interestingness. One of the main problems in this research field is the lack of visibility most works suffer since there is no standard terminology to refer to it, making it difficult to find and compare proposals and studies in the field. This contribution attempts to offer a well‐defined framework that allows researchers both to easily locate the previous proposals and to propose well‐grounded methods in the future. To accomplish both objectives, a two‐level taxonomy is proposed according to whether the time variable is considered to provide order to the data collection and to locate some temporal constraints, or whether it is considered as an attribute within the learning process. Some recent applications, available software tools, and a bibliographical analysis in accordance with the Web of Science are also shown. Finally, some critical considerations and potential further directions are discussed. This article is categorized under: Technologies > Association Rules Algorithmic Development > Association Rules

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Measuring the accuracy and interest of association rules: A new framework

Cited by 129 publications

References 5 publications

Using association rules to mine for strong approximate dependencies

Using association rules to mine for strong approximate dependencies

Fuzzy frameworks for mining data associations: fuzzy association rules and beyond

Temporal association rule mining: An overview considering the time variable as an integral or implied component

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